Correction: Tasci et al. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers 2023, 15, 2672

In the original publication [...].

In the original publication [1], there was a mistake shown in Tables 1-7 and Figures 3-5 as published.The pre/post-categorization information of four patients in our proteomic dataset was incorrectly labeled, requiring the proteomic analysis to be repeated.We repeated our analyses depending on the newly constructed, corrected, and normalized dataset.The newly obtained results are given in the corrected Tables 1-7 and Figures 3-5 below.

Discussion
Proteomic data are growing with the increased acquisition of biospecimens and the growth of protein panels generating increasingly large data.Proteomic data are expensive to acquire and often originate from a relatively small number of patients, resulting in a small number of instances for analysis overall.However, proteomic data are highly multidimensional [68], requiring feature engineering to optimize their use.The application of machine learning towards proteomic data is evolving [69] with applications towards protein-protein interactions [70], response to intervention [71], and biomarker discovery [68,72].The method we describe here is aimed at two interventions that occur concurrently, chemotherapy and radiation therapy in patients with a histologic diagnosis of GBM.Currently, no proteomic panels of this scale have been characterized for either radiation therapy or chemotherapy in GBM.Given that CRT is SOC, proteomic characterization of either intervention in isolation is not likely to be forthcoming.The goal of the method presented here was to define the alteration in the proteome in patients with GBM who undergo CRT in the most efficient manner possible, while also allowing for interpretable results that can be validated to advance the field.The maximum relevance minimum redundancy (mRMR) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and the five supervised learning models consisting of logistic regression, support vector machine, K nearest neighbors, random forest, and AdaBoost classification algorithms have all been employed in oncology settings including nomograms [73], genome-wide methylation analysis [74], and prediction of outcomes after CRT [75], however, not in the context of the proteomic alteration.Several results and conclusions emerged based on the comprehensive experimental results of our novel hybrid feature selection and rank-based weighting method (RadWise).Our novel rank-based feature weighting procedure identified relevant feature subsets with a cross-validation technique  S2).(A) Epidermal growth factor (EGF) (p-value of overlap 2.53 × 10 −7 ).(B) Catenin beta 1 (CTNNB1) (p-value of overlap 2.41 × 10 −6 ).(C) IPA-generated merged network for the 8 ML-identified proteins using the disease classification brain cancer.
Table 1.Accuracy rates: Five supervised learning models with or without feature selection.Color changes from red to green display performance results from the lowest (red) to the highest values (green).Proteinase-3 (PRTN3) Proteinase-3

ML-ACC
Yes, evolving role, may relate to pyroptosis, oxidative stress and immune response [59]

Figure 3 .
Figure 3.The visualization of the effects of the feature selection procedures with accuracy (ACC%) determined by a supervised learning method in conjunction with the feature selection approach (mRMR FS (yellow), LASSO FS (blue), and no FS (green)).

Figure 3 .
Figure 3.The visualization of the effects of the feature selection procedures with accuracy (ACC%) determined by a supervised learning method in conjunction with the feature selection approach (mRMR FS (yellow), LASSO FS (blue), and no FS (green)).

Figure 4 .
Figure 4.The effects of the number of features related to the minimum weight value using LASSO and mRMR-based feature selection with weighting methods.

Figure 5 .
Figure 5. Mean accuracy rate (ACC) vs. minimum weight stratified by model employed in analysis.

Figure 4 .
Figure 4.The effects of the number of features related to the minimum weight value using LASSO and mRMR-based feature selection with weighting methods.

Figure 4 .
Figure 4.The effects of the number of features related to the minimum weight value using LASSO and mRMR-based feature selection with weighting methods.

Figure 5 .
Figure 5. Mean accuracy rate (ACC) vs. minimum weight stratified by model employed in analysis.

Figure 5 .
Figure 5. Mean accuracy rate (ACC) vs. minimum weight stratified by model employed in analysis.

Table 1 .
Accuracy rates: Five supervised learning models with or without feature selection.Color changes from red to green display performance results from the lowest (red) to the highest values (green).

Table 1 .
Accuracy rates: Five supervised learning models with or without feature selection.Color changes from red to green display performance results from the lowest (red) to the highest values (green).

Table 2 .
Performance results (i.e., ACC%) using only LASSO-based feature selection and weighting methods.Color changes from red to green display performance results from the lowest (red) to the highest values (green).The bold value indicates the best result.

Table 3 .
Performance results (i.e., ACC%) using only mRMR-based feature selection and weighting methods.Color changes from red to green display performance results from the lowest (red) to the highest (green) values.The bold value indicates the best result.

Table 4 .
Mean performance results (i.e., ACC %, CV = 5) determined using both LASSO and mRMR-based feature selection with weighting methods.Color changes from red to green display performance results from the lowest (red) to the highest values (green).The bold value indicates the best result.

Table 5 .
The standard deviation of performance results (i.e., ACC %, CV = 5) determined using both LASSO and mRMR-based feature selection with weighting methods.Color changes from red to green display performance results from the lowest (red) to the highest values (green).

Table 6 .
Performance results without employing feature selection and feature weighting.Color changes from red to green display performance results from the lowest (red) to the highest values (green).

Table 7 .
Performance results employing LASSO and mRMR-based feature selection with weighting operation.Color changes from red to green display performance results from the lowest (red) to the highest values (green).

Table 8 .
Overview of the identified proteomic biomarkers illustrating the biological relevance to glioma.